论文标题
高维稀疏多元随机波动率模型
High-Dimensional Sparse Multivariate Stochastic Volatility Models
论文作者
论文摘要
尽管与Mgarch模型相比，多元随机波动率模型通常会产生更准确的预测，但它们的估计技术（例如贝叶斯MCMC）通常会受到维数的诅咒。我们根据受惩罚的OLS框架为MSV提出了一种快速有效的估计方法。将MSV模型指定为多元状态空间模型，我们执行了两步惩罚的程序。当参数差异时，我们提供了两步估计器的渐近属性和第一步估计器的oracle属性。通过模拟和财务数据来说明我们方法的性能。
Although multivariate stochastic volatility models usually produce more accurate forecasts compared to the MGARCH models, their estimation techniques such as Bayesian MCMC typically suffer from the curse of dimensionality. We propose a fast and efficient estimation approach for MSV based on a penalized OLS framework. Specifying the MSV model as a multivariate state space model, we carry out a two-step penalized procedure. We provide the asymptotic properties of the two-step estimator and the oracle property of the first-step estimator when the number of parameters diverges. The performances of our method are illustrated through simulations and financial data.
